Overview

Dataset statistics

Number of variables13
Number of observations2966
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.4 KiB
Average record size in memory104.0 B

Variable types

Numeric13

Alerts

gross_revenue is highly correlated with qtde_invoices and 4 other fieldsHigh correlation
recency_days is highly correlated with qtde_invoicesHigh correlation
qtde_invoices is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 4 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with gross_revenue and 2 other fieldsHigh correlation
gross_revenue is highly correlated with qtde_invoices and 1 other fieldsHigh correlation
qtde_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 1 other fieldsHigh correlation
qtde_products is highly correlated with qtde_invoicesHigh correlation
avg_ticket is highly correlated with qtde_returns and 2 other fieldsHigh correlation
qtde_returns is highly correlated with avg_ticket and 2 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_ticket and 2 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with avg_ticket and 2 other fieldsHigh correlation
gross_revenue is highly correlated with qtde_invoices and 2 other fieldsHigh correlation
qtde_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 4 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with qtde_items and 1 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with qtde_items and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qtde_invoices and 6 other fieldsHigh correlation
qtde_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 6 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_ticket is highly correlated with gross_revenue and 4 other fieldsHigh correlation
qtde_returns is highly correlated with gross_revenue and 4 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 4 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with gross_revenue and 4 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 53.41722918) Skewed
frequency is highly skewed (γ1 = 24.93321178) Skewed
qtde_returns is highly skewed (γ1 = 51.77236982) Skewed
avg_basket_size is highly skewed (γ1 = 44.65585181) Skewed
avg_unique_basket_size is highly skewed (γ1 = 44.65585181) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 34 (1.1%) zeros Zeros
qtde_returns has 1480 (49.9%) zeros Zeros

Reproduction

Analysis started2021-11-19 18:22:09.933326
Analysis finished2021-11-19 18:22:31.502148
Duration21.57 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2966
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2309.167229
Minimum0
Maximum5690
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:22:31.592488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.25
Q1926.25
median2113.5
Q33523.75
95-th percentile5011
Maximum5690
Range5690
Interquartile range (IQR)2597.5

Descriptive statistics

Standard deviation1547.505435
Coefficient of variation (CV)0.6701573693
Kurtosis-1.011941625
Mean2309.167229
Median Absolute Deviation (MAD)1265.5
Skewness0.3397296495
Sum6848990
Variance2394773.072
MonotonicityStrictly increasing
2021-11-19T15:22:31.702352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
30021
 
< 0.1%
29871
 
< 0.1%
29901
 
< 0.1%
29911
 
< 0.1%
29921
 
< 0.1%
29931
 
< 0.1%
29961
 
< 0.1%
29981
 
< 0.1%
29991
 
< 0.1%
Other values (2956)2956
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
56901
< 0.1%
56711
< 0.1%
56611
< 0.1%
56551
< 0.1%
56341
< 0.1%
56301
< 0.1%
56241
< 0.1%
56131
< 0.1%
56121
< 0.1%
56021
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2966
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.64633
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:22:31.823619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.25
Q113799.75
median15220.5
Q316769.5
95-th percentile17964.75
Maximum18287
Range5940
Interquartile range (IQR)2969.75

Descriptive statistics

Standard deviation1719.368253
Coefficient of variation (CV)0.1125930243
Kurtosis-1.206285243
Mean15270.64633
Median Absolute Deviation (MAD)1487
Skewness0.03191154775
Sum45292737
Variance2956227.19
MonotonicityNot monotonic
2021-11-19T15:22:31.945478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
175881
 
< 0.1%
149051
 
< 0.1%
161031
 
< 0.1%
146261
 
< 0.1%
148681
 
< 0.1%
182461
 
< 0.1%
171151
 
< 0.1%
166111
 
< 0.1%
159121
 
< 0.1%
Other values (2956)2956
99.7%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182691
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2951
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.410125
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:22:32.261727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile230.9525
Q1571.02
median1085.51
Q32310.295
95-th percentile7226.025
Maximum279138.02
Range279131.82
Interquartile range (IQR)1739.275

Descriptive statistics

Standard deviation10565.12098
Coefficient of variation (CV)3.842686431
Kurtosis355.1680075
Mean2749.410125
Median Absolute Deviation (MAD)672.255
Skewness16.79499336
Sum8154750.43
Variance111621781.3
MonotonicityNot monotonic
2021-11-19T15:22:32.381501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2053.022
 
0.1%
3312
 
0.1%
734.942
 
0.1%
1025.442
 
0.1%
598.22
 
0.1%
533.332
 
0.1%
731.92
 
0.1%
2092.322
 
0.1%
379.652
 
0.1%
745.062
 
0.1%
Other values (2941)2946
99.3%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
36.561
< 0.1%
451
< 0.1%
521
< 0.1%
52.21
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
70.021
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
136263.721
< 0.1%
124564.531
< 0.1%
116725.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.19386379
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:22:32.515124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.57405542
Coefficient of variation (CV)1.208434122
Kurtosis2.761755779
Mean64.19386379
Median Absolute Deviation (MAD)26
Skewness1.795023771
Sum190399
Variance6017.734074
MonotonicityNot monotonic
2021-11-19T15:22:32.628360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.3%
487
 
2.9%
385
 
2.9%
284
 
2.8%
876
 
2.6%
1067
 
2.3%
766
 
2.2%
966
 
2.2%
1764
 
2.2%
2255
 
1.9%
Other values (262)2217
74.7%
ValueCountFrequency (%)
034
 
1.1%
199
3.3%
284
2.8%
385
2.9%
487
2.9%
543
1.4%
766
2.2%
876
2.6%
966
2.2%
1067
2.3%
ValueCountFrequency (%)
3732
0.1%
3723
0.1%
3711
 
< 0.1%
3681
 
< 0.1%
3664
0.1%
3652
0.1%
3641
 
< 0.1%
3601
 
< 0.1%
3591
 
< 0.1%
3584
0.1%

qtde_invoices
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct57
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.722859069
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:22:32.751303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.845920929
Coefficient of variation (CV)1.545717066
Kurtosis190.2175037
Mean5.722859069
Median Absolute Deviation (MAD)2
Skewness10.74683007
Sum16974
Variance78.25031708
MonotonicityNot monotonic
2021-11-19T15:22:32.871615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2786
26.5%
3496
16.7%
4394
13.3%
5236
 
8.0%
1189
 
6.4%
6173
 
5.8%
7139
 
4.7%
898
 
3.3%
969
 
2.3%
1054
 
1.8%
Other values (47)332
11.2%
ValueCountFrequency (%)
1189
 
6.4%
2786
26.5%
3496
16.7%
4394
13.3%
5236
 
8.0%
6173
 
5.8%
7139
 
4.7%
898
 
3.3%
969
 
2.3%
1054
 
1.8%
ValueCountFrequency (%)
2061
< 0.1%
1981
< 0.1%
1241
< 0.1%
971
< 0.1%
911
< 0.1%
901
< 0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%

qtde_items
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1671
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1607.823668
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:22:33.008905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile103
Q1297
median641
Q31399
95-th percentile4408
Maximum196844
Range196842
Interquartile range (IQR)1102

Descriptive statistics

Standard deviation5885.360067
Coefficient of variation (CV)3.660451194
Kurtosis466.8037951
Mean1607.823668
Median Absolute Deviation (MAD)422
Skewness17.87136764
Sum4768805
Variance34637463.11
MonotonicityNot monotonic
2021-11-19T15:22:33.134231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
889
 
0.3%
1509
 
0.3%
848
 
0.3%
2468
 
0.3%
2608
 
0.3%
1348
 
0.3%
2728
 
0.3%
2888
 
0.3%
12007
 
0.2%
Other values (1661)2882
97.2%
ValueCountFrequency (%)
22
0.1%
122
0.1%
161
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
201
< 0.1%
231
< 0.1%
251
< 0.1%
261
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
809971
< 0.1%
798791
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
628121
< 0.1%
582431
< 0.1%
577721
< 0.1%

qtde_products
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct468
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.7508429
Minimum1
Maximum7837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:22:33.284230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7837
Range7836
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.3561638
Coefficient of variation (CV)2.194332499
Kurtosis354.1690203
Mean122.7508429
Median Absolute Deviation (MAD)44
Skewness15.67332496
Sum364079
Variance72552.74296
MonotonicityNot monotonic
2021-11-19T15:22:33.409229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2846
 
1.6%
2038
 
1.3%
3535
 
1.2%
1533
 
1.1%
2932
 
1.1%
1132
 
1.1%
1932
 
1.1%
2530
 
1.0%
2630
 
1.0%
2730
 
1.0%
Other values (458)2628
88.6%
ValueCountFrequency (%)
15
 
0.2%
214
0.5%
316
0.5%
417
0.6%
526
0.9%
628
0.9%
718
0.6%
819
0.6%
927
0.9%
1027
0.9%
ValueCountFrequency (%)
78371
< 0.1%
55861
< 0.1%
50951
< 0.1%
45771
< 0.1%
26971
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16721
< 0.1%
16361
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2964
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.93158379
Minimum2.150588235
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:22:33.551587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.914341758
Q113.11982258
median17.94687607
Q324.98612169
95-th percentile90.50125
Maximum56157.5
Range56155.34941
Interquartile range (IQR)11.86629911

Descriptive statistics

Standard deviation1037.458318
Coefficient of variation (CV)19.9774057
Kurtosis2887.787214
Mean51.93158379
Median Absolute Deviation (MAD)5.975052174
Skewness53.41722918
Sum154029.0775
Variance1076319.761
MonotonicityNot monotonic
2021-11-19T15:22:33.689296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.478333332
 
0.1%
4.1622
 
0.1%
18.152222221
 
< 0.1%
12.9491
 
< 0.1%
16.293720931
 
< 0.1%
36.244117651
 
< 0.1%
29.784166671
 
< 0.1%
22.87926231
 
< 0.1%
20.511041671
 
< 0.1%
149.0251
 
< 0.1%
Other values (2954)2954
99.6%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
56157.51
< 0.1%
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.38185752
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:22:33.807438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q126
median48.39285714
Q385.33333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.33333333

Descriptive statistics

Standard deviation63.55905304
Coefficient of variation (CV)0.9432665613
Kurtosis4.883245776
Mean67.38185752
Median Absolute Deviation (MAD)26.27380952
Skewness2.062428243
Sum199854.5894
Variance4039.753224
MonotonicityNot monotonic
2021-11-19T15:22:33.932444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1425
 
0.8%
7021
 
0.7%
421
 
0.7%
720
 
0.7%
3518
 
0.6%
4918
 
0.6%
4617
 
0.6%
1117
 
0.6%
2117
 
0.6%
516
 
0.5%
Other values (1248)2776
93.6%
ValueCountFrequency (%)
116
0.5%
1.51
 
< 0.1%
213
0.4%
2.51
 
< 0.1%
2.6013986011
 
< 0.1%
315
0.5%
3.3214285711
 
< 0.1%
3.3303571431
 
< 0.1%
3.52
 
0.1%
421
0.7%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3631
 
< 0.1%
3621
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1223
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1134147061
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:22:34.068558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.008892185955
Q10.01633986928
median0.0258760183
Q30.04926691295
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.03292704367

Descriptive statistics

Standard deviation0.4080017981
Coefficient of variation (CV)3.597432926
Kurtosis991.9622892
Mean0.1134147061
Median Absolute Deviation (MAD)0.01217738816
Skewness24.93321178
Sum336.3880183
Variance0.1664654673
MonotonicityNot monotonic
2021-11-19T15:22:34.184809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1197
 
6.6%
0.062517
 
0.6%
0.0277777777817
 
0.6%
0.0238095238116
 
0.5%
0.0909090909115
 
0.5%
0.0833333333315
 
0.5%
0.0344827586214
 
0.5%
0.0294117647114
 
0.5%
0.0769230769213
 
0.4%
0.0256410256413
 
0.4%
Other values (1213)2635
88.8%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
31
 
< 0.1%
26
 
0.2%
1.1428571431
 
< 0.1%
1197
6.6%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53083109921
 
< 0.1%
0.53
 
0.1%

qtde_returns
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.12373567
Minimum0
Maximum80995
Zeros1480
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:22:34.311986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1513.254167
Coefficient of variation (CV)24.35871171
Kurtosis2762.791257
Mean62.12373567
Median Absolute Deviation (MAD)1
Skewness51.77236982
Sum184259
Variance2289938.175
MonotonicityNot monotonic
2021-11-19T15:22:34.451669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01480
49.9%
1164
 
5.5%
2147
 
5.0%
3105
 
3.5%
489
 
3.0%
678
 
2.6%
561
 
2.1%
1251
 
1.7%
743
 
1.4%
843
 
1.4%
Other values (204)705
23.8%
ValueCountFrequency (%)
01480
49.9%
1164
 
5.5%
2147
 
5.0%
3105
 
3.5%
489
 
3.0%
561
 
2.1%
678
 
2.6%
743
 
1.4%
843
 
1.4%
941
 
1.4%
ValueCountFrequency (%)
809951
< 0.1%
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33311
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1974
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.6203673
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:22:34.577224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44.05555556
Q1103.3083333
median172.125
Q3281.6442308
95-th percentile599.7
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.3358974

Descriptive statistics

Standard deviation791.9269277
Coefficient of variation (CV)3.172525288
Kurtosis2253.629865
Mean249.6203673
Median Absolute Deviation (MAD)82.875
Skewness44.65585181
Sum740374.0094
Variance627148.2588
MonotonicityNot monotonic
2021-11-19T15:22:34.710342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
869
 
0.3%
739
 
0.3%
829
 
0.3%
758
 
0.3%
1408
 
0.3%
608
 
0.3%
1368
 
0.3%
888
 
0.3%
Other values (1964)2878
97.0%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
40498.51
< 0.1%
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
28011
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1974
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.6203673
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-19T15:22:34.845069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44.05555556
Q1103.3083333
median172.125
Q3281.6442308
95-th percentile599.7
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.3358974

Descriptive statistics

Standard deviation791.9269277
Coefficient of variation (CV)3.172525288
Kurtosis2253.629865
Mean249.6203673
Median Absolute Deviation (MAD)82.875
Skewness44.65585181
Sum740374.0094
Variance627148.2588
MonotonicityNot monotonic
2021-11-19T15:22:34.968424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
869
 
0.3%
739
 
0.3%
829
 
0.3%
758
 
0.3%
1408
 
0.3%
608
 
0.3%
1368
 
0.3%
888
 
0.3%
Other values (1964)2878
97.0%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
40498.51
< 0.1%
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
28011
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%

Interactions

2021-11-19T15:22:29.596052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:12.788424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:14.236738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:15.618987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:16.917326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:18.399650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:19.662579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:21.068311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:22.575513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:23.909729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:25.283724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:26.777727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:28.191319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:29.697224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:12.936430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:14.328762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:15.715561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:17.019557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:18.497246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:19.760816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:21.165414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:22.673513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:24.013550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:25.379726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:26.882596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:28.289390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:29.802019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:13.056678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:14.424558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:15.811934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:17.115614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:18.592378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:19.862739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:21.264127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:22.771497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:24.112098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:25.479605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:27.000487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:28.393417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:29.912207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:13.166795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:14.521754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:15.913801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:17.224463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:18.685610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:19.970238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:21.365641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:22.867022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:24.214303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:25.576499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:27.100669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:28.499823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:30.024986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:13.277249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:14.624836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:16.026069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:17.336398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:18.781389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:20.081236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:21.470012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:22.975761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:24.316641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:25.682686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:27.209195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:28.608162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:30.124719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:13.376727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:14.715829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:16.115481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:17.436538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:18.869503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:20.180237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:21.562420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:23.067643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:24.415701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:25.774990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:27.308066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:28.707283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:30.234766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:13.488470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:14.820966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:16.220233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:17.548502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:18.976700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:20.297534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:21.668732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:23.171859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:24.529368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:25.883296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:27.419721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:28.819429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:30.357665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:13.596489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:14.929526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:16.323523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:17.662245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:19.076486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:20.418322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:21.773854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:23.283291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:24.642562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:25.997329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:27.531584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:28.934098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:30.488670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:13.703369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:15.028934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:16.415746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:17.763641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:19.169207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:20.523704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:22.000076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:23.380775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:24.740079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:26.096736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:27.635684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:29.039261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:30.610478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:13.807170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:15.214024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:16.514752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:17.874404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:19.265313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:20.632620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:22.110761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:23.480025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:24.844177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:26.201650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:27.739888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:29.148624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:30.731628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:13.912047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:15.313584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:16.612617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:17.980902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:19.359545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:20.736896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:22.222786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:23.582639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:24.948986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:26.303943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:27.846051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:29.258221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:30.852625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:14.025297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:15.414242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:16.710615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:18.087662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:19.461240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:20.846405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:22.336141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:23.694516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:25.063647image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:26.410311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:27.961939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:29.372757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:30.982820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:14.133528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:15.517552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:16.813586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:18.289811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:19.562083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:20.954229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:22.461715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:23.801988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:25.177068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:26.670413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:28.073813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T15:22:29.484679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-19T15:22:35.085631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-19T15:22:35.284667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-19T15:22:35.454091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-19T15:22:35.621285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-19T15:22:31.186304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-19T15:22:31.401360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
00178505391.2100372.000034.00001733.0000297.000018.152235.500017.000040.000050.970650.9706
11130473232.590056.00009.00001390.0000171.000018.904027.25000.028335.0000154.4444154.4444
22125836705.38002.000015.00005028.0000232.000028.902523.18750.040350.0000335.2000335.2000
3313748948.250095.00005.0000439.000028.000033.866192.66670.01790.000087.800087.8000
4415100876.0000333.00003.000080.00003.0000292.00008.60000.073222.000026.666726.6667
55152914623.300025.000014.00002102.0000102.000045.326523.20000.040129.0000150.1429150.1429
66146885630.87007.000021.00003621.0000327.000017.219818.30000.0572399.0000172.4286172.4286
77178095411.910016.000012.00002057.000061.000088.719835.70000.033541.0000171.4167171.4167
881531160767.90000.000091.000038194.00002379.000025.54354.14440.2433474.0000419.7143419.7143
99160982005.630087.00007.0000613.000067.000029.934847.66670.02440.000087.571487.5714

Last rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
29565602177271060.250015.00001.0000645.000066.000016.06446.00001.00006.0000645.0000645.0000
2957561217232421.52002.00002.0000203.000036.000011.708912.00000.15380.0000101.5000101.5000
2958561317468137.000010.00002.0000116.00005.000027.40004.00000.40000.000058.000058.0000
2959562413596697.04005.00002.0000406.0000166.00004.19907.00000.25000.0000203.0000203.0000
29605630148931237.85009.00002.0000799.000073.000016.95682.00000.66670.0000399.5000399.5000
2961563412479473.200011.00001.0000382.000030.000015.77334.00001.000034.0000382.0000382.0000
2962565514126706.13007.00003.0000508.000015.000047.07533.00000.750050.0000169.3333169.3333
29635661135211092.39001.00003.0000733.0000435.00002.51124.50000.30000.0000244.3333244.3333
2964567115060301.84008.00004.0000262.0000120.00002.51531.00002.00000.000065.500065.5000
2965569012558269.96007.00001.0000196.000011.000024.54186.00001.0000196.0000196.0000196.0000